Spatio-temporal Outlier Detection Based on Context: A Summary of Results

Spatio-temporal outlier detection plays an important role in some applications fields such as geological disaster monitoring, geophysical exploration, public safety and health etc. For the current lack of contextual outlier detection for spatio-temporal dataset, spatio-temporal outlier detection based on context is proposed. The pattern is to discover anomalous behavior without contextual information in space and time, and produced by using a graph based random walk model and composite interest measures. Our approach has many advantages including producing contextual spatio-temporal outlier, and fast algorithms. The algorithms of context-based spatio-temporal outlier detection and improved method are proposed. The effectiveness of our methods is justified by empirical results on real data sets. It shows that the algorithms are effective and validate.